Electric vehicle optimal scheduling method considering multiple satisfaction degrees of users and new energy consumption

Abstract

The invention discloses an electric vehicle optimal scheduling method considering multiple satisfaction degrees of users and new energy consumption. The method comprises the steps of extracting user travel information from a user travel information database based on a Monte Carlo method, simulating a charging process, and establishing a physical condition and a charging expectation of scheduling participated by a single user, so as to create an EV cluster user satisfaction index. User satisfaction and new energy consumption are comprehensively considered, and a distributed power grid joint scheduling model is constructed by taking the lowest system operation cost as an objective function. And establishing a coupling satisfaction index model and a satisfaction evaluation mechanism by combining Sweden customer satisfaction indexes. And based on user satisfaction and distributed power grid new energy consumption, comparing the four scheduling schemes to obtain an optimal strategy. According to the electric vehicle optimal scheduling method considering the multiple satisfaction degrees of the users and the new energy consumption, the wind and light abandoning rate can be reduced, the satisfaction degrees of the users and the satisfaction degrees of the new energy consumption are remarkably improved, and friendly interaction among the electric vehicles, the power grid and the new energy is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle cluster optimization scheduling for fast charging stations, and in particular to an electric vehicle optimization scheduling method that takes into account multiple user satisfaction and new energy consumption. Background Art

[0002] With the increasing capacity of new energy grid-connected and the number of electric vehicles, the difficulty of regulation, operation stability and economic problems faced by the power system are becoming increasingly prominent. In order to give full play to the potential energy storage resource attributes of electric vehicles as flexible and dispatchable power grids, an electric vehicle optimization scheduling strategy considering multiple user satisfaction and new energy consumption is proposed. In the process of energy transformation and power system transformation, relying solely on power supply side resources can no longer meet the needs of the power system. Demand side resources such as electric vehicles (EVs) have become important regulation resources with their high flexibility. Through the optimal scheduling of EV clusters, the power imbalance of the power system can be alleviated, the utilization rate of charging facilities can be improved, the charging cost can be reduced, and more convenient and reliable charging services can be provided to EV users, realizing the friendly interaction of vehicle-grid-source. Summary of the invention

[0003] The purpose of the present invention is to provide an electric vehicle optimization scheduling method that takes into account multiple user satisfaction and new energy consumption.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] S1. Based on the Monte Carlo method, user travel information is extracted from the user travel information database, and the charging process is simulated to establish the physical conditions and charging expectations of a single user participating in scheduling, so as to create an EV cluster user satisfaction index.

[0006] S2. Taking into account user satisfaction and new energy consumption, and taking the lowest system operating cost as the objective function, a distributed power grid joint dispatching model is constructed.

[0007] S3. Combined with the Swedish customer satisfaction index, a coupled satisfaction index model and satisfaction evaluation mechanism are established.

[0008] S4. Based on user satisfaction and the consumption of new energy in distributed power grids, four scheduling schemes are compared to obtain the optimal strategy.

[0009] As a preferred technical solution of the present invention, the step S1: EV cluster user satisfaction index includes a charging model based on single user travel information, and its expression is as follows:

[0010] 1) Single user travel information uses a one-dimensional matrix I:

[0011] I=[CP d DCo TS oc P r SF]

[0012] Where: C represents the willingness to travel, with a value of 1 for travel and 0 for no travel; P d represents the travel destination; D represents the travel distance; C o represents the number of commutes, with a value of 2; T represents the travel time sequence number; S oc Indicates the EV charge state; P r Indicates the charging and discharging power; S indicates the EV charging amount; and F indicates the charging fee.

[0013] 2) Total time period during which users access charging piles:

[0014]

[0015] Where: t nhw represents the commuting period; t hw Indicates non-commuting hours; U ev represents the total time period during which users access charging piles in a day; u(t) represents the unit step function; v represents the EV driving speed; CO represents the total number of commutes in a day.

[0016] 3) EV daily charging situation:

[0017]

[0018] Where: Indicates the state of charge when charging begins; Indicates the desired state of charge at the end of charging; represents the average charging power; η c Indicates the charging efficiency coefficient; T all Indicates the total charging time; S ev Indicates EV capacity; Indicates the total amount of charging in a day; T n1 and T n2 Indicates the first and second charging time.

[0019] As a preferred technical solution of the present invention, the step S1: single-user schedulable physical conditions and charging expectation indicators are expressed as follows:

[0020] 1) Real-time scheduling capacity:

[0021]

[0022] Where: Indicates the maximum charge and discharge power in the T period; P pr Indicates the average charge and discharge power.

[0023] 2) Charging needs during travel:

[0024]

[0025] Where: Indicates the battery level before commuting; Indicates the minimum state of charge expected by the user; P mile Indicates the power consumption per unit mileage of an EV.

[0026] 3) Charge state limitation:

[0027]

[0028] Where: Indicates real-time charge status; Indicates the highest state of charge expected by the user.

[0029] 4) Total charging capacity required:

[0030] S=P pr η c (T n1 +T n2 )

[0031] 5) Charging cost:

[0032] F=r c,T P pr (T n1 +T n2 )

[0033] Where: r c,T Indicates the charging price during period T.

[0034] As a preferred technical solution of the present invention, the step S1: EV cluster user satisfaction index includes, and its expression is as follows:

[0035] 1) Real-time scheduling capacity limit C 1 :

[0036]

[0037] Where: N represents the maximum charge and discharge limit of the EV cluster in the T period; hw represents the number of non-commuting EVs; η d Indicates the average discharge efficiency coefficient of EV; and It represents the discharge and charge amount of the EV cluster during the T period.

[0038] 2) Real-time charging capacity A 1 :

[0039]

[0040] Where: represents the minimum power required by the cluster before the first and second trips of EV users; R av Indicates the user's driving distance; N nhw represents the number of commuting EVs; Indicates EV power consumption per unit distance; S ev,t Indicates the real-time power of the EV cluster; and Indicates minimum and maximum charge limits; and Indicates the minimum charging limit during commuting and non-commuting hours, usually

[0041] 3) Charge state limit C 2 :

[0042]

[0043] Where: and represents the minimum and maximum limits of the state of charge of the EV cluster during the T period, is the charge state of the EV cluster during the T period.

[0044] 4) Total charging capacity required D 1 :

[0045]

[0046] Where: S end represents the total amount of charging of the EV cluster after the daily scheduling is completed; S n and It represents the total daily charging amount of the nth user and EV cluster.

[0047] 5) Minimum charging cost C 3 :

[0048]

[0049] Where: U f represents the EV cluster charging cost after the intraday dispatch is completed; r d,T represents the EV discharge price during period T.

[0050] As a preferred technical solution of the present invention, the step S2: based on the EV cluster user travel satisfaction index and considering the new energy consumption rate, constructing a joint scheduling model includes:

[0051] 1) Objective function:

[0052]

[0053] Where: and represents the fuel cost, exhaust emission cost and active power of thermal power unit i in period T; a i 、b i and c i It represents the fuel cost characteristic curve coefficient of thermal power unit i in the T period; and represents the correlation coefficient of exhaust gas treatment of thermal power unit i; represents the total penalty cost for abandoning wind and solar power during the T period; C w , C pv Indicates the penalty price for abandoning wind and solar power; It indicates the amount of wind and solar power abandoned by wind turbines and photovoltaic generators during the period T.

[0054] 2) Overall scheduling objectives:

[0055]

[0056] Where: N g Indicates the number of thermal power generators; min{} means finding the minimum value.

[0057] 3) Output upper and lower limits:

[0058]

[0059] 4) Climbing rate constraint:

[0060]

[0061] Where: represents the active output of the i-th thermal power unit in the T period; P i min , P i max represents the minimum and maximum active output of the i-th thermal power unit; R down,i , R up,i It represents the maximum down-regulation and up-regulation of active power of thermal power unit i in the T period; It indicates the active power output of the i-th thermal power unit in the (T-1) time period.

[0062] 5) Constraints on new energy output:

[0063]

[0064] Where: Indicates the grid-connected power of wind power and photovoltaic power in the T period; Indicates the maximum power generation of wind power and photovoltaic power in the T period.

[0065] 6) System power balance constraints:

[0066]

[0067] Where: N g Total active power of thermal power generators; Indicates the total grid-connected power of wind power and photovoltaic power; Indicates the EV cluster discharge and charging power; P load Indicates the system base load.

[0068] As a preferred technical solution of the present invention, the step S3: user satisfaction and new energy consumption evaluation mechanism includes:

[0069] 1) User satisfaction evaluation mechanism includes:

[0070] Real-time scheduling capacity limit indicator C 1 The evaluation criteria for the relationship with user satisfaction are:

[0071]

[0072] Where: V C1 Indicates about C 1 The actual user satisfaction of the indicator; Represents C 1 The score value of the indicator in the T period.

[0073] 2) Real-time charging indicator A 1 The evaluation criteria for the relationship with user satisfaction are:

[0074]

[0075] Where: V A1 About A 1 The actual user satisfaction of the indicator; Indicates A 1 The score value of the indicator in the T period; represents the power of the EV cluster during period T; It represents the minimum power demand of the EV cluster in period T.

[0076] 3) Charge state limit index C 2 The evaluation criteria for the relationship with user satisfaction are:

[0077]

[0078] Where: V C2 Indicates about C 2 The actual user satisfaction of the indicator; Represents C 2 The score value of the indicator in the T period.

[0079] 4) Total charging capacity demand index D 1 , the total charging amount at the end of scheduling is used for judgment, and the judgment criteria are:

[0080] V D1 =20%S 4

[0081]

[0082] Where: V D1 About D 1 The actual user satisfaction of the indicator; S 4 Indicates D 1 The score value of the indicator;

[0083] 5) Minimum charging cost index C 3 , the minimum charging cost is selected under different scheduling modes, and the evaluation criteria are:

[0084] V C3 =20%S 5

[0085]

[0086] Where: V C3 Indicates about C 3 The actual user satisfaction of the indicator; S 4 Represents C 3 The score value of the indicator; U T Indicates the actual charging cost; Indicates the minimum charging cost.

[0087] 6) User satisfaction (US) is described as:

[0088] U S =V C1 +V A1 +V C2 +V D1 +V C3

[0089] 7) The new energy consumption evaluation mechanism includes:

[0090] Wind power consumption index I 1 :

[0091]

[0092] Where: Indicates the abandoned air volume during period T; Indicates the total amount of waste; represents the total wind power generation; and Indicates the actual wind abandonment rate, maximum and minimum wind abandonment rate; I w1 Indicates wind power consumption assessment index 1; I w2 Indicates wind power fluctuation assessment index 2; τ 1 , τ 2 Represents the relevant weight coefficient, which is 0.5.

[0093] Photovoltaic consumption index I 2 :

[0094]

[0095] Where: Indicates the amount of abandoned light in period T; Indicates the total amount of abandoned light; Represents the total photovoltaic power generation; and Indicates the actual abandoned light rate, maximum and minimum abandoned light rate; I pv1 Indicates photovoltaic consumption assessment index 1; I pv2 Indicates photovoltaic fluctuation assessment index 2; T all is the total photovoltaic power generation time period; τ 3 , τ 4 Represents the relevant weight coefficient, which is 0.5.

[0096] New energy consumption index I new :

[0097] I new =(I 1 C w +I 2 C pv ) / (C w +C pv )

[0098] Where: C w , C pv Represents the installed capacity of wind power and photovoltaic power.

[0099] The present invention also provides a computer-readable storage medium, which stores a program or instruction. The program or instruction is a step for a computer to execute the method for optimizing the scheduling of electric vehicles taking into account multiple user satisfaction and new energy consumption.

[0100] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention provides an electric vehicle optimization scheduling method that considers multiple user satisfaction and new energy consumption. Compared with considering user satisfaction only from the distribution network side, the present invention implements a joint scheduling strategy for the distributed power grid system, which can reduce the wind and solar abandonment rate, significantly improve user satisfaction and new energy consumption satisfaction, and realize friendly interaction between electric vehicles, power grids, and new energy. Among them, compared with participating in scheduling on the distribution network side, large-scale EV clusters participate in the joint scheduling of transmission and distribution networks, which can effectively improve the system's ability to consume new energy. At the same time, the multiple satisfaction index model established from multiple angles can effectively improve user satisfaction, promote users to voluntarily participate in scheduling, and form a friendly interaction and virtuous cycle among vehicles, networks, and sources. Furthermore, the scheduling model established by considering multiple user satisfaction and new energy consumption satisfaction will bring more constraints and impose more restrictions on system operation compared to considering only one of the two, but it will not reduce the effect of a single indicator; on the contrary, the two complement each other and can achieve a win-win situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0102] Figure 2 This is a load curve diagram of the embodiment of the present invention without considering the new energy consumption scheduling scheme (Scheme 1);

[0103] Figure 3 The source-load curves of the new energy consumption scheduling scheme according to the embodiment of the present invention are shown in FIG. 1 , wherein (a) is the source-load curve of scheme 2, (b) is the source-load curve of scheme 3, and (c) is the source-load curve of scheme 4;

[0104] Figure 4 EV real-time dispatch capacity curves of embodiments of the present invention, wherein (a) is the EV real-time dispatch capacity curve of scheme 2, (b) is the EV real-time dispatch capacity curve of scheme 3, and (c) is the EV real-time dispatch capacity curve of scheme 4;

[0105] Figure 5 EV real-time net charge capacity curves of embodiments of the present invention, wherein (a) is the EV real-time net charge capacity curve of scheme 2, (b) is the EV real-time net charge capacity curve of scheme 3, and (c) is the EV real-time net charge capacity curve of scheme 4;

[0106] Figure 6 The graphs of renewable energy power generation and grid-connected power generation of the embodiments of the present invention are as follows, wherein (a) is the curve of renewable energy power generation and grid-connected power generation of Scheme 1, (b) is the curve of renewable energy power generation and grid-connected power generation of Scheme 2, (c) is the curve of renewable energy power generation and grid-connected power generation of Scheme 3, and (d) is the curve of renewable energy power generation and grid-connected power generation of Scheme 4; DETAILED DESCRIPTION

[0107] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0108] Example 1

[0109] This embodiment provides an electric vehicle optimization scheduling method that takes into account multiple user satisfaction and new energy consumption, such as Figure 1 As shown, the method comprises the following steps:

[0110] Step 1: Based on the Monte Carlo method, user travel information is extracted from the user travel information database, and the charging process is simulated to establish the physical conditions and charging expectations of a single user participating in scheduling, so as to create an EV cluster user satisfaction index.

[0111] The specific steps include:

[0112] The EV cluster user satisfaction index includes a charging model based on single user travel information:

[0113] The single user travel information uses a one-dimensional matrix I:

[0114] I=[CP d DC o TS oc P r SF]

[0115] Where: C represents the willingness to travel, with a value of 1 for travel and 0 for no travel; P d represents the travel destination; D represents the travel distance; C o represents the number of commutes, with a value of 2; T represents the travel time sequence number; S oc Indicates the EV charge state; P r Indicates the charging and discharging power; S indicates the EV charging amount; and F indicates the charging fee.

[0116] Total time period during which users access charging piles during the day:

[0117]

[0118] Where: t nhw represents the commuting period; t hw Indicates non-commuting hours; U ev represents the total time period during which users access charging piles in a day; u(t) represents the unit step function; v represents the EV driving speed; CO represents the total number of commutes in a day.

[0119] EV daily charging situation:

[0120]

[0121] Where: Indicates the state of charge when charging begins; Indicates the desired state of charge at the end of charging; represents the average charging power; η c Indicates the charging efficiency coefficient; T all Indicates the total charging time; S ev Indicates EV capacity; Indicates the total amount of charging in a day; T n1 and T n2 Indicates the first and second charging time.

[0122] The single-user dispatchable physical conditions and charging expectation indicators include:

[0123] Real-time dispatch capacity:

[0124]

[0125] Where: Indicates the maximum charge and discharge power in the T period, P pr Indicates the average charge and discharge power.

[0126] Travel charging needs:

[0127]

[0128] Where: Indicates the battery level before commuting; Indicates the minimum state of charge expected by the user; P mile Indicates the power consumption per unit mileage of an EV.

[0129] State of Charge Limits:

[0130]

[0131] Where: Indicates real-time charge status; Indicates the highest state of charge expected by the user.

[0132] Total charging capacity required:

[0133] S=P pr η c (T n1 +T n2 )

[0134] Charging cost:

[0135] F=r c,T P pr (Tn1 +T n2 )

[0136] Where: r c,T Indicates the charging price during period T.

[0137] EV cluster user satisfaction indicators include:

[0138] Real-time scheduling capacity limit C 1 :

[0139]

[0140] Where: N represents the maximum charge and discharge limit of the EV cluster in the T period; hw represents the number of non-commuting EVs; η d Indicates the average discharge efficiency coefficient of EV.

[0141] When the dispatch center issues an order, its order dispatch capacity must be lower than the dispatchable capacity of the EV cluster:

[0142]

[0143] Where: and It represents the discharge and charge amount of the EV cluster during the T period.

[0144] Real-time charging capacity A 1 :

[0145]

[0146] Where: represents the minimum power required by the cluster before the first and second trips of EV users; R av Indicates the user's driving distance; N nhw represents the number of commuting EVs; Indicates EV power consumption per unit distance; S ev,t Indicates the real-time power of the EV cluster; and Indicates minimum and maximum charge limits; and Indicates the minimum charging limit during commuting and non-commuting hours, usually

[0147] State of charge limit C 2 :

[0148]

[0149] Where: and Indicates the minimum and maximum limits of the state of charge of the EV cluster during the T period; is the charge state of the EV cluster during the T period.

[0150] Total charging capacity required D 1 :

[0151]

[0152] Where: S end represents the total amount of charging of the EV cluster after the daily scheduling is completed; S n and It represents the total daily charging amount of the nth user and EV cluster.

[0153] Minimum charging cost C 3 :

[0154]

[0155] Where: U f represents the EV cluster charging cost after the intraday dispatch is completed; r d,T represents the EV discharge price during period T.

[0156] Step 2: Considering user satisfaction and new energy consumption comprehensively, and taking the lowest system operation cost as the objective function, a distributed power grid joint dispatching model is constructed.

[0157] The specific steps include:

[0158] Based on the EV cluster user travel satisfaction index and considering the new energy consumption rate, a joint dispatch model is constructed, including:

[0159] Objective function:

[0160]

[0161] Where: and represents the fuel cost, exhaust emission cost and active power of thermal power unit i in period T; a i , b i and c i It represents the fuel cost characteristic curve coefficient of thermal power unit i in the T period; and represents the correlation coefficient of exhaust gas treatment of thermal power unit i; represents the total penalty cost for abandoning wind and solar power during the T period; C w , C pv Indicates the penalty price for abandoning wind and solar power; It indicates the amount of wind and solar power abandoned by wind turbines and photovoltaic generators during the period T.

[0162] Overall scheduling objectives:

[0163]

[0164] Where: N g Indicates the number of thermal power generators; min{} indicates finding the minimum value;

[0165] The constraints include upper and lower limits of output and climbing rate constraints:

[0166] Output upper and lower limits:

[0167]

[0168] Ramp rate constraint:

[0169]

[0170] Where: represents the active power output of the i-th thermal power unit in the T period; P i min , P i max represents the minimum and maximum active output of the i-th thermal power unit; R down,i , R up,i It represents the maximum downward and upward adjustment of active power of thermal power unit i in the T period; It indicates the active power output of the i-th thermal power unit in the (T-1) time period.

[0171] New energy output constraints:

[0172]

[0173] Where: Indicates the grid-connected power of wind power and photovoltaic power in the T period; Indicates the maximum power generation of wind power and photovoltaic power in the T period.

[0174] The power balance constraints of the system:

[0175]

[0176] Where: N g Total active power of thermal power generators; Indicates the total grid-connected power of wind power and photovoltaic power; Indicates the EV cluster discharge and charging power; P load Indicates the system base load.

[0177] Step 3: Combine the Swedish customer satisfaction index to establish a coupled satisfaction index model and satisfaction evaluation mechanism.

[0178] The specific steps include:

[0179] User satisfaction and new energy consumption evaluation mechanisms include:

[0180] The user satisfaction evaluation mechanism includes:

[0181] Real-time scheduling capacity limit indicator C 1 Evaluation criteria for the relationship with user satisfaction:

[0182]

[0183] Where: V C1 Indicates about C 1 The actual user satisfaction of the indicator; Represents C 1 The score value of the indicator in the T period.

[0184] Real-time charging indicator A 1 Evaluation criteria for the relationship with user satisfaction:

[0185]

[0186] Where: V A1 About A 1 The actual user satisfaction of the indicator; Indicates A 1 The score value of the indicator in the T period; represents the power of the EV cluster during period T; It represents the minimum power demand of the EV cluster in period T.

[0187] State of charge limit indicator C 2 Evaluation criteria for the relationship with user satisfaction:

[0188]

[0189] Where: V C2 Indicates about C 2 The actual user satisfaction of the indicator; Represents C 2 The score value of the indicator in the T period.

[0190] Total charging capacity requirement index D 1 , the total charging amount after scheduling is judged, and the judgment criteria are:

[0191] V D1 =20%S 4

[0192]

[0193] Where: V D1 About D1 The actual user satisfaction of the indicator; S 4 Indicates D 1 The score value of the indicator.

[0194] Minimum charging cost index C 3 , the minimum charging cost is selected under different scheduling modes, and the evaluation criteria are:

[0195] V C3 =20%S 5

[0196]

[0197] Where: V C3 Indicates about C 3 The actual user satisfaction of the indicator; S 4 Represents C 3 The score value of the indicator; U T Indicates the actual charging cost; Indicates the minimum charging cost.

[0198] User satisfaction (US) is described as:

[0199] U S =V C1 +V A1 +V C2 +V D1 +V C3

[0200] The new energy consumption evaluation mechanism includes:

[0201] Wind power consumption index I 1 :

[0202]

[0203] Where: Indicates the abandoned air volume during period T; Indicates the total amount of waste; represents the total wind power generation; and Indicates the actual wind abandonment rate, maximum and minimum wind abandonment rate; I w1 Indicates wind power consumption assessment index 1; I w2 Indicates wind power fluctuation assessment index 2; τ 1 , τ 2 Represents the relevant weight coefficient, which is 0.5.

[0204] Photovoltaic consumption index I 2 :

[0205]

[0206] Where: Indicates the amount of abandoned light in period T; Indicates the total amount of abandoned light; Represents the total photovoltaic power generation; and Indicates the actual abandoned light rate, maximum and minimum abandoned light rate; I pv1 Indicates photovoltaic consumption assessment index 1; I pv2 Indicates photovoltaic fluctuation assessment index 2; T all is the total photovoltaic power generation time period; τ 3 , τ 4 Represents the relevant weight coefficient, which is 0.5.

[0207] New energy consumption index I new :

[0208] I new =(I 1 C w +I 2 C pv ) / (C w +C pv )

[0209] Where: C w , C pv Represents the installed capacity of wind power and photovoltaic power.

[0210] Example 2

[0211] Reference Figures 3 to 6 , which is the second embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a verification test of an electric vehicle optimization scheduling method that takes into account multiple user satisfaction levels and new energy consumption. In order to verify and illustrate the technical effects used in this method, the test results are compared by scientific argumentation to verify the actual effect of this method.

[0212] Parameter setting: EV average charging power Charge and discharge efficiency coefficient η c =η d =0.9, EV capacity S ev =30kW·h; ambient temperature is 25℃, charge and discharge ratio is fixed, and the number of electric vehicle charging cycles is within 1000 times; EV power consumption per unit distance Set the time periods [7:00, 9:00] and [17:00, 19:00] as rush hour commuting periods.

[0213] Table 1 Thermal power units:

[0214] Table 1Parameters of thermal power units

[0215]

[0216] Table 2 Power output parameters:

[0217] Table 2Parameters of power outputs

[0218]

[0219] Four different dispatching schemes are set. Scheme 1 does not consider the dispatching scheme of new energy consumption, and schemes 2, 3, and 4 consider the dispatching scheme of new energy consumption. Scheme 2: Considering the new energy consumption on the power generation side, the EV operator sets the real-time price (RTP), and considers the state of charge limit index C. 2 , D 1 and C 3 , implement orderly charging and discharging scheduling, Scheme 3: Based on Scheme 2, add real-time scheduling capacity limit index C 1 , to avoid the dispatch center issuing large-capacity dispatch instructions to the EV cluster during the commuting period, which would affect user travel. Solution 4: Based on Solution 3, add real-time charging capacity indicator A 1 , ensuring that the overall charging capacity of the EV cluster meets demand during commuting hours.

[0220] Use MATLB to conduct simulation experiments and output test results.

[0221] Figure 2 This is the load curve diagram without considering the new energy consumption scheduling plan (Scheme 1).

[0222] Under the condition of meeting the same charging amount, the orderly charging EV cluster will charge during the low electricity price period, reaching the power required for the first commute before 7:00 and the power required for the second commute before 17:00, thereby reducing the total charging cost; it has a certain peak-shaving and valley-filling effect, making the distribution network load curve smoother; but the scheduling is only carried out on the distribution network side, and the fluctuation of new energy output on the power generation side and its absorption are not taken into account.

[0223] Figure 3 It is the source-load curve considering the new energy consumption scheduling plan.

[0224] Among the three options, the charging and discharging of the EV cluster can actively respond to the grid-connected renewable energy power, discharging when the renewable energy output drops suddenly, and charging when the renewable energy output increases suddenly.

[0225] Figure 4 This is the EV real-time scheduling capacity curve of the embodiment of the present invention.

[0226] All three solutions satisfy C 3 Indicator requirements, but in the joint dispatch of the transmission and distribution network, if C is set without considering whether the user is in the commuting period, 1 Indicators, the dispatch center may issue dispatch capacity exceeding the limit, reducing the user's C 1 Satisfaction index.

[0227] Figure 5 This is a real-time net charge capacity curve of an EV according to an embodiment of the present invention.

[0228] In the first commuting period [7:00, 9:00], before the trip (i.e., the 28th sampling period), the real-time power values ​​of the three schemes were 21.5328MW·h, 21.6907MW·h, and 20.2267MW·h, respectively, all of which met the requirement that the power values ​​were greater than the expected values. D 1 Indicator requirements; however, in the second commuting period [17:00, 19:00], before the trip (i.e. the 68th sampling period), the real-time power values ​​were 21.3097MW·h, 30.5705MW·h and 50MW·h respectively, and only scheme 4 met the power value greater than the expected value D 1 Indicator requirements. In Schemes 2 and 3, the minimum EV power indicator is set at 15MW in all time periods, which violates the minimum charging cost requirement. Therefore, in Scheme 4, different standards are set for daytime and nighttime during non-commuting hours to reduce charging costs.

[0229] Figure 6 It is the curve of renewable energy power generation and grid-connected power according to the embodiment of the present invention.

[0230] Table 3 Comparison of new energy consumption

[0231]

[0232] Compared with dispatching distribution networks based on user satisfaction, Schemes 2 to 4 consider the joint dispatching of transmission and distribution networks to accommodate new energy on the power generation side, and their new energy absorption capacity is significantly improved, among which Scheme 4 has the most significant effect.

[0233] User satisfaction evaluation.

[0234] Table 4 Satisfaction index for new energy consumption

[0235]

[0236] Table 5User satisfaction indicators

[0237]

[0238] It can be seen from Tables 4 and 5 that under the joint scheduling based on user satisfaction, Scheme 4 takes into account process satisfaction on the basis of result satisfaction, has the highest comprehensive satisfaction, and has the best effect on new energy consumption.

[0239] In summary, compared with considering user satisfaction only from the distribution network side, the electric vehicle optimization scheduling strategy proposed in the present invention that considers multiple user satisfaction levels and new energy consumption can reduce the wind and solar power abandonment rate, significantly improve user satisfaction and new energy consumption satisfaction, and achieve friendly interaction among electric vehicles, power grids, and new energy.

[0240] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An electric vehicle optimization scheduling method considering multiple user satisfaction and new energy consumption, characterized in that: include: S1. Based on the Monte Carlo method, user travel information is extracted from the user travel information database, and the charging process is simulated to establish the physical conditions and charging expectations of a single user participating in scheduling, so as to create an EV cluster user satisfaction index. S2. Taking into account user satisfaction and new energy consumption, and taking the lowest system operating cost as the objective function, a distributed power grid joint dispatching model is constructed. S3. Combined with the Swedish customer satisfaction index, a coupled satisfaction index model and satisfaction evaluation mechanism are established. S4. Based on user satisfaction and the consumption of new energy in distributed power grids, four scheduling schemes are compared to obtain the optimal strategy.

2. The electric vehicle optimization scheduling method considering multiple user satisfaction and new energy consumption according to claim 1 is characterized in that: The EV cluster user satisfaction index includes a charging model based on single user travel information: 1) Single user travel information uses a one-dimensional matrix I: Where: Indicates travel intention, with a value of 1 for travel and 0 for no travel; Indicates the travel destination; Indicates the travel distance; Indicates the number of commuting times, the value is 2; Indicates the travel time sequence number; Indicates the EV charge state; Indicates the charge and discharge power; Indicates the EV charging level; Indicates the charging cost. 2) Total time period during which users access charging piles: Where: Indicates commuting time; Indicates non-commuting hours; Indicates the total time period during which users access charging piles within a day; represents the unit step function; represents the EV driving speed; CO represents the total number of commutes per day. 3) EV daily charging situation: Where: Indicates the state of charge when charging begins; Indicates the desired state of charge at the end of charging; Indicates the average charging power; Indicates the charging efficiency coefficient; Indicates the total charging time; Indicates EV capacity; Indicates the total amount of charging in a day; and Indicates the first and second charging time.

3. The electric vehicle optimization scheduling method considering multiple user satisfaction and new energy consumption according to claim 1 is characterized in that: The single-user dispatchable physical conditions and charging expectation indicators include: 1) Real-time scheduling capacity: Where: Indicates the maximum charge and discharge power during the T period; Indicates the average charge and discharge power. 2) Charging needs during travel: Where: Indicates the battery level before commuting; Indicates the minimum state of charge expected by the user; Indicates the power consumption per unit mileage of an EV. 3) State of charge limitation: Where: Indicates real-time charge status; Indicates the highest state of charge expected by the user. 4) Total charging capacity requirement: 5) Charging cost: Where: Indicates the charging price during period T.

4. The electric vehicle optimization scheduling method considering multiple user satisfaction and new energy consumption according to claim 1 is characterized in that: The EV cluster user satisfaction indicators include: 1) Real-time scheduling capacity limitations : Where: Indicates the maximum charge and discharge limit of the EV cluster during the T period; represents the number of non-commuting EVs; Indicates the average discharge efficiency coefficient of EV; and It represents the discharge and charge amount of the EV cluster during the T period. 2) Real-time charging capacity : Where: , It represents the minimum amount of electricity that the cluster needs to have before the first and second trips of EV users; Indicates the user's travel distance; represents the number of commuting EVs; Indicates EV power consumption per unit distance; Indicates the real-time power of the EV cluster; and Indicates minimum and maximum charge limits; and Indicates the minimum charging limit during commuting and non-commuting hours, usually . 3) State of charge limitation : Where: and represents the minimum and maximum limits of the state of charge of the EV cluster during the T period, is the charge state of the EV cluster during the T period. 4) Total charging capacity required : Where: It represents the total amount of charging of the EV cluster after the intraday scheduling is completed; and It represents the total daily charging amount of the nth user and EV cluster. 5) Minimum charging cost : Where: It represents the EV cluster charging cost after the intraday dispatch is completed; represents the EV discharge price during period T.

5. The electric vehicle optimization scheduling method considering multiple user satisfaction and new energy consumption according to claim 1 is characterized in that: Based on the EV cluster user travel satisfaction index and considering the new energy consumption rate, a joint dispatch model is constructed, including: 1) Objective function: Where: , and Indicates that the thermal power unit is Fuel costs, exhaust emission costs and active power during the time period; , and Indicates that thermal power unit i is Fuel cost characteristic curve coefficient within the time period; , and represents the correlation coefficient of exhaust gas treatment of thermal power unit i; express Total penalty costs for wind and solar curtailment during the period; , Indicates the penalty price for abandoning wind and solar power; , Indicates wind turbines and photovoltaic generators The amount of wind and solar power abandoned during the period. 2) Overall scheduling objectives: Where: Indicates the number of thermal power generators; Indicates finding the minimum value. 3) Output upper and lower limit constraints: 4) Climbing rate constraint: Where: It represents the active power output of the i-th thermal power unit in the T period; , represents the minimum and maximum active output of the i-th thermal power unit; , Indicates that thermal power unit i is The maximum decrease and increase of active power within the time period; It indicates the active power output of the i-th thermal power unit in the (T-1) time period. 5) Constraints on new energy output: Where: , Indicates the grid-connected power of wind power and photovoltaic power in the T period; , Indicates the maximum power generation of wind power and photovoltaic power in the T period. 6) System power balance constraints: Where: express Total active power of thermal power generators; , Indicates the total grid-connected power of wind power and photovoltaic power; , Indicates the EV cluster discharge and charging power; Indicates the system base load.

6. The electric vehicle optimization scheduling method considering multiple user satisfaction and new energy consumption according to claim 1 is characterized by: User satisfaction and new energy consumption evaluation mechanisms include: 1) User satisfaction evaluation mechanism includes: Real-time dispatch capacity limit indicator The evaluation criteria for the relationship with user satisfaction are: Where: Indicates about The actual user satisfaction of the indicator; express The score value of the indicator in the T period. 2) Real-time charging indicator The evaluation criteria for the relationship with user satisfaction are: Where: Indicates about The actual user satisfaction of the indicator; express The score value of the indicator in the T period; represents the power of the EV cluster during period T; It represents the minimum power demand of the EV cluster in period T. 3) State of charge limit indicator The evaluation criteria for the relationship with user satisfaction are: Where: Indicates about The actual user satisfaction of the indicator; express The score value of the indicator in the T period. 4) Total charging capacity demand indicator , the total charging amount at the end of scheduling is judged by the following criteria: Where: Indicates about The actual user satisfaction of the indicator; express The score value of the indicator. 5) Minimum charging cost indicator , the minimum charging cost is selected under different scheduling modes, and the evaluation criteria are: Where: Indicates about The actual user satisfaction of the indicator; express The score value of the indicator; Indicates the actual charging cost; Indicates the minimum charging cost. 6) User satisfaction (US) is described as: 7) The new energy consumption evaluation mechanism includes: Wind power consumption index : Where: Indicates the abandoned air volume during period T; Indicates the total amount of waste; represents the total wind power generation; , and Indicates the actual wind abandonment rate, maximum and minimum wind abandonment rate; It indicates wind power consumption assessment index 1; It represents wind power fluctuation assessment index 2; , Represents the relevant weight coefficient, which is 0.

5. Photovoltaic consumption index : Where: Indicates the amount of abandoned light in period T; Indicates the total amount of abandoned light; Represents the total photovoltaic power generation; , and Indicates the actual abandoned light rate, maximum and minimum abandoned light rate; Indicates photovoltaic consumption assessment index 1; Indicates photovoltaic fluctuation assessment index 2; is the total photovoltaic power generation time period; , Represents the relevant weight coefficient, which is 0.

5. 8) New energy consumption indicators : Where: , Represents the installed capacity of wind power and photovoltaic power.

7. A computer-readable storage medium, characterized in that: It includes one or more programs for execution by one or more processors of an electronic device, and the one or more programs include instructions for executing the electric vehicle optimization scheduling method considering multiple user satisfaction and new energy consumption as described in any one of claims 1-6.

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